Denolle-Lab · mdenolle · Aug 10, 2026 · Aug 11, 2026 · Aug 11, 2026 · Aug 11, 2026
diff --git a/literature/figs/demo_10_deviations.png b/literature/figs/demo_10_deviations.png
diff --git a/literature/figs/demo_11_multiverse.png b/literature/figs/demo_11_multiverse.png
diff --git a/literature/figs/demo_12_bayes.png b/literature/figs/demo_12_bayes.png
diff --git a/literature/figs/demo_13_branch_asymmetry.png b/literature/figs/demo_13_branch_asymmetry.png
diff --git a/literature/figs/demo_14_network_pairs.png b/literature/figs/demo_14_network_pairs.png
diff --git a/literature/figs/demo_15_window_envelope.png b/literature/figs/demo_15_window_envelope.png
diff --git a/literature/figs/demo_1_methods.png b/literature/figs/demo_1_methods.png
diff --git a/literature/figs/demo_2_aggregation.png b/literature/figs/demo_2_aggregation.png
diff --git a/literature/figs/demo_3_uncertainty.png b/literature/figs/demo_3_uncertainty.png
diff --git a/literature/figs/demo_4_frequency_depth.png b/literature/figs/demo_4_frequency_depth.png
diff --git a/literature/figs/demo_5_window_band.png b/literature/figs/demo_5_window_band.png
diff --git a/literature/figs/demo_6_stacking.png b/literature/figs/demo_6_stacking.png
diff --git a/literature/figs/demo_7_reference.png b/literature/figs/demo_7_reference.png
diff --git a/literature/figs/demo_8_artifacts.png b/literature/figs/demo_8_artifacts.png
diff --git a/literature/figs/demo_9_multiverse.png b/literature/figs/demo_9_multiverse.png
diff --git a/literature/figs/realdata_1_validation.png b/literature/figs/realdata_1_validation.png
diff --git a/literature/figs/realdata_2_interferograms.png b/literature/figs/realdata_2_interferograms.png
diff --git a/literature/figs/realdata_3_warmup.png b/literature/figs/realdata_3_warmup.png
diff --git a/paper/manuscript_marine.qmd b/paper/manuscript_marine.qmd
diff --git a/paper/manuscript_marine.tex b/paper/manuscript_marine.tex
diff --git a/src/codameter/deviations.py b/src/codameter/deviations.py
@@ -431,6 +431,7 @@ def fig_deviation_ranking(rows=None):
 def fig_multiverse_full(mv=None):
     """The ultimate multiverse: every pipeline + the variance attribution."""
     import matplotlib.pyplot as plt
+    from matplotlib.colors import Normalize
 
     if mv is None:
         mv = multiverse()
@@ -444,7 +445,7 @@ def fig_multiverse_full(mv=None):
     # (a) fan of pipelines, coloured by RMS error with a colourblind-safe,
     # perceptually uniform sequential map (dark = accurate, bright = biased).
     order = np.argsort(-np.nan_to_num(rms))
-    norm = plt.Normalize(np.nanpercentile(rms, 5), np.nanpercentile(rms, 95))
+    norm = Normalize(np.nanpercentile(rms, 5), np.nanpercentile(rms, 95))
     cmap = plt.cm.viridis_r
     for i in order:
         ax[0].plot(yrs, curves[i] * PCT, color=cmap(norm(rms[i])), lw=0.3, alpha=0.16)
@@ -463,13 +464,10 @@ def fig_multiverse_full(mv=None):
     )
     ax[0].plot(yrs, truth * PCT, color=C["truth"], lw=2.6, label="ground truth")
     ax[0].axvline(2.0, color="0.6", ls="--", lw=1)
-    # Clip tightly to the truth scale; the cycle-skipping pipelines run off-axis
-    # (that is the point — the colourbar flags them) but would otherwise swamp the
+    # Fixed, symmetric range: the cycle-skipping pipelines run off-axis (that is
+    # the point -- the colourbar flags them) but would otherwise swamp the
     # signal and make the panel unreadable.
-    span = (np.nanmax(truth) - np.nanmin(truth)) * PCT
-    ax[0].set_ylim(
-        (np.nanmin(truth) * PCT - 0.35 * span, np.nanmax(truth) * PCT + 0.35 * span)
-    )
+    ax[0].set_ylim((-0.8, 0.8))
     ax[0].set(
         xlabel="time (years)",
         ylabel="dv/v (%)",

diff --git a/src/codameter/uq_bayes.py b/src/codameter/uq_bayes.py
@@ -409,11 +409,11 @@ def _fig_bayes(res, run):
     truth = run.truth
     sd_cd = np.sqrt(np.diag(res.Cd))
     sd_post = np.sqrt(np.diag(res.mu_cov))
-    fig = plt.figure(figsize=(7.2, 3.0), layout="constrained")
-    gs = fig.add_gridspec(1, 3, width_ratios=[1.5, 1.0, 1.1])
+    fig = plt.figure(figsize=(7.2, 5.6), layout="constrained")
+    gs = fig.add_gridspec(2, 2, height_ratios=[1.0, 1.0])
 
     # (a) ensemble + posterior + the two bands.
-    ax0 = fig.add_subplot(gs[0])
+    ax0 = fig.add_subplot(gs[0, 0])
     for k in range(run.members.shape[0]):
         ax0.plot(yrs, run.members[k] * 100, lw=0.5, color="0.7", alpha=0.6)
     if truth is not None:
@@ -448,7 +448,7 @@ def _fig_bayes(res, run):
     )
 
     # (b) the data covariance matrix.
-    ax1 = fig.add_subplot(gs[1])
+    ax1 = fig.add_subplot(gs[0, 1])
     vmax = float(np.percentile(np.diag(res.Cd), 85))  # robust to the warm-up spike
     im = ax1.imshow(
         res.Cd,
@@ -472,7 +472,9 @@ def _fig_bayes(res, run):
     fig.colorbar(im, ax=ax1, fraction=0.046)
 
     # (c) time-dependent sigma_d(t) and the effective-sample-size collapse.
-    ax2 = fig.add_subplot(gs[2])
+    # Full width on its own row: it carries four legend entries and was too
+    # horizontally squeezed sharing a row with (a) and (b).
+    ax2 = fig.add_subplot(gs[1, :])
     ax2.plot(
         yrs, sd_cd * 100, color=C["volcano"], lw=1.6, label=r"$\sigma_d(t)$ (total)"
     )